Systems and methods utilizing machine learning driven rules engine for dynamic data-driven enterprise application

ABSTRACT

The present invention generally relates to system, method and graphical user interface for executing one or more tasks in dynamic data driven enterprise application. The invention includes creation of rules on a rule creation interface by one or more syntax from a rule creation syntax data library. The system of the invention is configured to identify optimum rule to process one or more tasks. The invention provides machine learning models driven rule engine for executing the tasks wherein an AI engine invokes dynamic conditions of the rules to execute the task.

BACKGROUND 1. Technical Field

The present invention relates generally to rules engine in enterpriseapplications. More particularly, the invention relates to systems,methods and computer program product for machine learning driven rulesengine for dynamic data-driven enterprise application.

2. Description of the Prior Art

Enterprise applications support organization in executing multipleoperations depending on the domain like insurance, retail, supply chainetc. Depending on the complexity of the operations or tasks to beexecuted and ever-changing needs, each organization requires fasterprocessing and automation of functions through certain pre-configuredrules. Most rules are logic based that utilize static conditions andexpress operational specific constraints. For e.g.; in supply chaindomain a rule may include conditional logic to approve an invoicetransaction based on an upper limit set by the rule. However, inenterprise application such as supply chain management applications, thestatic conditions are unable to capture complex scenarios such asdynamically computed risk-based conditions, complex contract management,inventory management, or related supply chain scenarios.

The complexity of supply chain domain may require infinite number ofrules to process each operation or task which makes it cumbersome andalmost impossible to determine the type of rule to be processed forexecuting the operation or task. Further, in real time data processingenvironment, the rules need to be configured to process updated datasetsfor executing tasks. In case additional attributes needs to beprocessed, the rules become obsolete and are unable to execute theoperation. Moreover, in real time processing scenarios, the rules arenot integrated to the backend data processing in the enterpriseapplication, thereby not allowing structuring of machine learning basedprocessing decisions.

Further, in case of a decentralized system, the enterprise applicationoperations present multiple challenges like security vulnerabilities,privacy leakage, complicated authorization and workflow inefficienciesetc. Further, blockchain implemented systems present other uniquechallenges. Data processing in complex supply chain management systemswith blockchain built sub-systems, present substantive technicalchallenges including model identification for processing datasetsrelated to blockchain transactions and contracts. Such, systems requirefrequent access to and interaction with blockchain network, which is notonly costly but also resource consuming. Executing any operation bycreating rules, say, for processing invoice in a blockchain implementedsystem is extremely complex requiring processing of data at multipledata layers. Further, the blockchain implemented systems requirespecific security structure to enable secured access while executing anytask.

In view of the above problems, there is a need for system and method forexecuting one or more tasks in an enterprise application that canovercome the problems associated with the prior arts.

SUMMARY

According to an embodiment, the present invention provides a system,method and graphical user interface for executing one or more tasks in adynamic data driven enterprise application. The method includesgenerating a GUI, wherein the GUI includes a first input component forreceiving a first input indicating the one or more tasks to be executedthrough the GUI, receiving the first input indicating the one or moretasks to be executed via the first input component, wherein an AI enginecoupled to the processing device identifies a plurality of rulesconfigured for executing the one or more tasks, in response toidentification of a plurality of rules configured to execute the one ormore tasks, generating and rendering within the GUI a list of optimumrules from the plurality of rules, receiving a second input indicatingat least one rule from the list of optimum rules for executing the oneor snore tasks via a second input component, wherein the at least onerule represents a set of syntax structured to execute the one or moretasks wherein the set of syntax is previously generated by analyzinghistorical data related to the one or more tasks through the AI engine,and triggering one or more machine learning models based on the secondinput to obtain a second output from the one or more machine learningmodels to be rendered within the GUI indicating execution of the one ormore task wherein the one or more machine learning models is integratedinto a rule engine coupled to the processor for processing the at leastone rule to execute the one or more tasks.

In an embodiment, the present invention provides a system and method forgenerating a graphical user interface (GUI) for rule creation and one ormore task execution in enterprise application. The system includes aprocessing device and a memory device including instructions that areexecutable by the processing device for causing the processing device togenerate the GUI, wherein the GUI includes a first input component forreceiving an input indicating one or more data attributes of the one ormore task to be executed through the GUI. The method includes processingthe first input by an AI engine to determine existence of a rule in ahistorical rule database for processing the task and rendering the firstoutput on the GUI. The method includes the step of generating a rulecreation interface on the GUI if the first output indicates absence ofany rule to process the task. The method includes the step of receivinga second input in response to the first output through a second inputcomponent on the GUI for creating at least one rule to execute the oneor more tasks wherein the at least one rule represents a set of syntaxstructured on the GUI to execute the one or more tasks. The methodincludes the step of identifying and triggering one or more machinelearning models based on the second input to obtain a second output fromthe one or more machine learning models to be rendered within the GUIindicating execution of the one or more task wherein the one or moremachine learning models is integrated into a rule engine coupled to theprocessor for processing the at least one rule to execute the one ormore tasks.

In an embodiment, the method for executing one or more tasks in anenterprise application includes receiving at least one rule on a rulecreation interface by a user to execute one or more tasks wherein a rulecreation syntax data library is provided on the interface for enablingthe user to create the at least one rule; and identifying and triggeringone or more machine learning (ML) models related to the at least onerule for processing the at least one rule to execute the one or moretask, wherein the one or more machine learning models is integrated intoa rule engine coupled to the processor for processing the at least onerule to execute the one or more tasks.

In an embodiment, the system and method of the present inventionincludes one or more data scripts configured for generating the list ofoptimum rules based on a rule evaluation, wherein the one or more datascripts are backend scripts created by a bot based on the first inputand AI processing for enabling automation of identifying and generatingthe optimum rules from the plurality of rules.

In an embodiment, the present invention provides a non-transitorycomputer-readable storage medium coupled to one or more processors andhaving instructions stored thereon which, when executed by the one ormore processors, cause the one or more processors to perform a methodfor executing one or more tasks in an enterprise application.

In an embodiment, the one or more Machine learning (ML) models includeclassification model, regression model, recommendation model andclustering/anomaly detection model. Further, the one or more machinelearning models create a standardized integration with each type of theone or more machine learning models in the rule engine.

In an advantageous aspect, the system and method of the presentinvention enables rule creation for processing of one or more enterpriseapplication task through a graphical user interface configured toprovide a syntax data library with a plurality of components that enablecreation of one or more rules driven by one or more machine learningmodels. Further, the system and method of the invention is configured toevaluate a plurality of rules that execute one or more tasks in anenterprise application. The rule evaluation enables the system toidentify at least one optimum rule for processing the task to beexecuted. The invention utilized Artificial intelligence for ruleevaluation through data scripts.

In yet another embodiment, the system includes a blockchain networkhaving one or more data blocks connected to each other and configuredfor storing SCM application data. The system creates rule forintegrating the AI engine with one or more external entity systemsthrough the blockchain network, wherein any transaction through theblockchain network is encrypted using a random number and the one ormore tasks in the one or more SCM application functions is executedthrough the blockchain network.

In an advantageous aspect, the present invention utilizes MachineLearning algorithms, rule engine, artificial intelligence engine,prediction data models, and data analysis for rule generation to executeof one or more tasks in the enterprise application.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be better understood and when consideration is givento the drawings and the detailed description which follows. Suchdescription makes reference to the annexed drawings wherein:

FIG. 1 is a view of a system for executing one or more tasks in anenterprise application in accordance with an embodiment of theinvention.

FIG. 1A is a view of a machine learning operation framework of modeldriven rule engine for dynamic data driven enterprise application inaccordance with an example embodiment of the invention.

FIG. 1B is a rule engine block diagram of the system in accordance withan embodiment of the invention.

FIG. 2 is a flowchart depicting a method for executing one or more tasksin an enterprise application in accordance with an embodiment of theinvention.

FIG. 2A is a flowchart depicting a method for executing one or moretasks through optimum rule in an enterprise application in accordancewith an embodiment of the invention.

FIG. 3 is a user interface for rule creation in accordance with anembodiment of the invention.

FIG. 3A is a user interface showing conversion of visual representationto code in accordance with an embodiment of the invention.

FIG. 3B is a user interface expression builder of the system inaccordance with an embodiment of the invention.

FIG. 4 is a user interface showing a list of rules including systemconfigured rules, rules created on a rule creation user interface andrules created by extension tools in accordance with an embodiment of theinvention.

FIG. 4A is a user interface showing a list of rules with versions inaccordance with an embodiment of the invention.

FIG. 5 is a rule creation interface for purchase order (PO) creationtask in accordance with an embodiment of the invention.

FIG. 5A is a rule creation interface associating a PO value and showingproperties and rules characteristics in accordance with an embodiment ofthe invention.

FIG. 5B is a rule creation interface showing rule for checking supplierrating with logical operators in accordance with an embodiment of theinvention.

FIG. 5C is a rule creation interface creating rule for duplicate findertask in accordance with an embodiment of the invention.

FIG. 5D is a rule creation interface creating rule for checkingprobability of on time delivery considering the changing parameters inaccordance with an embodiment of the invention.

FIG. 5E is a rule creation interface creating a set of test cases to runvalues and find the outcome from the rule to ensure that Rule is workingand delivering results in accordance with an embodiment of theinvention.

FIG. 6 is a flow diagram depicting creation of purchase order relatedtask in accordance with an embodiment of the invention.

FIG. 7 is a flow diagram depicting identification of conflicting ruleswhile executing a task in accordance with an embodiment of theinvention.

DETAILED DESCRIPTION

Described herein are the various embodiments of the present invention,which includes method and system for executing one or more tasks in adynamic data driven enterprise application including a supply chainmanagement application.

The various embodiments including the example embodiments will now bedescribed more fully with reference to the accompanying drawings, inwhich the various embodiments of the invention are shown. The inventionmay, however, be embodied in different forms and should not be construedas limited to the embodiments set forth herein. Rather, theseembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of the invention to thoseskilled in the art. In the drawings, the sizes of components may beexaggerated for clarity.

It will be understood that when an element or layer is referred to asbeing “on,” “connected to,” or “coupled to” another element or layer, itcan be directly on, connected to, or coupled to the other element orlayer or intervening elements or layers that may be present. As usedherein, the term “and/or” includes any and all combinations of one ormore of the associated listed items.

Spatially relative terms, such as “rule creation,” “syntax datalibrary,” or “optimum rules,” and the like, may be used herein for easeof description to describe one element or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. It willbe understood that the spatially relative terms are intended toencompass different orientations of the structure in use or operation inaddition to the orientation depicted in the figures.

The subject matter of various embodiments, as disclosed herein, isdescribed with specificity to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different features orcombinations of features similar to the ones described in this document,in conjunction with other technologies. Generally, the variousembodiments including the example embodiments relate to a system andmethod for executing one or more tasks in a dynamic data drivenenterprise application.

In an exemplary embodiment, the present invention provides a machinelearning driven rules engine for dynamic data driven enterpriseapplication to execute one or more tasks including supply chainmanagement tasks. The invention provides a graphical user interface(GUI) with one or more graphical elements structured on the interface toenable a user to execute one or more rule creation, rule finder, ruleconflict or rule execution tasks. The GUI receives an input through aninput component of the graphical element, the input indicates a task tobe executed. The input and associated data attributes of the task areprocessed by an AI engine to determine intent of the task for triggeringa plurality of rules stored in a historical rules database to identifyan optimum rule for executing the task. In absence of a rule, the GUIenables a user to create rule based on a rule creation syntax datalibrary provided on the rule creation interface to execute the taskthrough rule creation interface of the GUI. The rule is processed by oneor more machine learning models related to the rule for executing thetask. The machine learning models are integrated into the rule engine.The rule engine process one or more tasks in an enterprise applicationdeveloped through a codeless platform. While rules engines are used toexecute discrete logic that needs precision, machine learning, isfocused on taking a number of inputs and trying to predict an outcome.

In an embodiment, the rule includes system configured rules created byan enterprise application codeless platform, rules created by extensiontools, and rules created on the rules creation interface of the GUI.

Referring to FIG. 1 , a system 100 for executing one or more tasks in anenterprise application is provided in accordance with an embodiment ofthe invention. The system 100 is implemented over a layered codelessplatform architecture having a data layer 101, a foundation layer 102, ashared framework layer 103, an application layer 104, and acustomization layer 105. Each layer of the architecture includes aplurality of configurable components interacting with each other toexecute at least one operation of the SCM enterprise application. Itshall be apparent to a person skilled in the art that while FIG. 1provides certain configurable components, the nature of the componentsitself enables redesigning of the platform architecture throughaddition, deletion, modification of the configurable components andtheir positioning in the layered architecture. Such addition,modification of configurable components depending on the nature of thearchitecture layer function shall be within the scope of this invention.

The system 100 also includes one or more entity machines including anapplication admin user machine 106, a citizen developer user machine106A and a platform developer user machine 106B. The system 100 includesa network 107 configured for communicating with one or more elements ofthe system for execution of one or more tasks in enterpriseapplications. The system 100 includes a server 108 configured forreceiving data and instructions from the entity machines (106, 106A,106B). The system 100 includes application rule creation and evaluationtool 109 having a plurality of support architecture components forperforming various prediction and analysis through AI engine withmultiple functions including processing of historical rules, Artificialintelligence (AI) based processing of application datasets, creation ofone or more rules and data models configured to process differentparameters etc.

In an embodiment the entity machines (106, 106A, 106B) may communicatewith the server 108 wirelessly through communication interface, whichmay include digital signal processing circuitry. Also, the entitymachines (106, 106A, 106B) may be implemented in a number of differentforms, for example, as a smartphone, computer, personal digitalassistant, or other similar devices.

The computing devices referred to as the entity machine, server,processor etc. of the present invention are intended to representvarious forms of digital computers, such as laptops, desktops,workstations, personal digital assistants, and other appropriatecomputers. Computing device of the present invention further intend torepresent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smartphones, and other similarcomputing devices. The components shown here, their connections andrelationships, and their functions, are meant to be exemplary only, andare not meant to limit implementations of the inventions describedand/or claimed in this disclosure.

In an embodiment, the system is provided in a cloud or cloud-basedcomputing environment. The underlying codeless development architectureof the system enables more secured processes.

In an embodiment the server 108 of the invention may include varioussub-servers for communicating and processing data across the network.The sub-servers include but are not limited to content managementserver, application server, directory server, database server, mobileinformation server and real-time communication server.

In example embodiment the server 108 shall include electronic circuitryfor enabling execution of various steps by one or more processors. Theelectronic circuity has various elements including but not limited to aplurality of arithmetic logic units (ALU) and floating-point Units(FPU's). The ALU enables processing of binary integers to assist information of at least one table of rule syntax where, the domain modelsimplemented for domain characteristic prediction are applied to the datatable for obtaining rules for executing one or more tasks in dynamicdata driven enterprise application. In an example embodiment the serverelectronic circuitry includes at least one Athematic logic unit (ALU),floating point units (FPU), other processors, memory, storage devices,high-speed interfaces connected through buses for connecting to memoryand high-speed expansion ports, and a low-speed interface connecting tolow-speed bus and storage device. Each of the components of theelectronic circuitry, are interconnected using various busses, and maybe mounted on a common motherboard or in other manners as appropriate.The processor can process instructions for execution within the server108, including instructions stored in the memory or on the storagedevices to display graphical information for the graphical userinterface (GUI) on an external input/output device, such as displaycoupled to high-speed interface. In other implementations, multipleprocessors and/or multiple busses may be used, as appropriate, alongwith multiple memories and types of memory. Also, multiple servers maybe connected, with each server providing portions of the necessaryoperations (e.g., as a server bank, a group of blade servers, or amulti-processor system).

In an example embodiment, the system of the present invention includes afront-end web server communicatively coupled to at least one databaseserver, where the front-end web server is configured to process inputreceived through the rule creation interface or extension tool interfaceby the one or more processors based on one or more domain models andapplying an AI based dynamic processing logic to automate rule creationand task execution actions through the GUI.

In a related embodiment, the extension tool is a programming toolconfigured as per the application domain for enabling an applicationuser/admin user to create rules for processing one or more tasks in anenterprise application. The extension tool may be any editor that isextended with plugins as per domain requirement. It provides anintegrated development environment with GUI for enabling domain specificstructuring of applications. The extension tool is configured forcreating one or more domain specific syntax data library to create rulefor executing one or more SCM application task.

The application user or application admin user interface 106 of thesystem 100 includes an application interface 110 having rule creation UIand rule evaluation UI for executing the one or more tasks in theenterprise application.

The citizen developer user interface 106A of the system 100 includes acodeless development application UI, entity configurator, data basequery engine, configurable data source repository, expression builderand plugin creator.

In an embodiment, the system 100 includes a memory data store/data lake111 configured for storing data related to enterprise application, rulecreation UI for creating at least one rule to execute one or more tasksin an enterprise application etc. Further, the system 100 includes asupport architecture 112 configured for enabling interacting ofapplication rule creation and evaluation tool 109, memory data store111, entity machines (106, 106A, 106B) and server 108. The supportarchitecture 112 includes an AI engine 113 coupled to one or moreprocessors 114 configured for processing one or more SCM applicationtasks. The support architecture 112 also includes a plurality of IOTdevices 115 configured to capturing, transmitting, receiving data andinstructions from one or more computing devices. The plurality of IOTdevices 115 is configured to provide the inputs to the server 108 oninitiation of the SCM action wherein the IOT devices include sensor,mobile, camera, Bluetooth, RF tags and similar devices or combinationthereof. Further, the inputs may include but is not limited to inventorymanagement data or warehouse management data or data related to one ormore SCM application operation etc.

In an embodiment, the present invention uses GPUs (Graphical processingunits) for enabling AI engine 113 to provide computing power toprocesses humongous amount of data.

In an exemplary embodiment, the AI engine 113 employs machine learningtechniques that learn patterns and generate insights from the data forenabling automation of operations. Further, the AI engine 113 with MLemploys deep learning that utilizes artificial neural networks to mimicneural network. The artificial neural networks analyze data to determineassociations and provide meaning to unidentified or new dataset.

The support architecture 112 also includes a plurality of API(application programming interface) 116 configured for plugging aspectsof AI (Artificial Intelligence) into datasets for identifying rules toexecute the tasks including PO creation, invoice, inventory managementor any other supply chain management task. Further, the API is alsoconsumed by the bots and mobile applications.

In an exemplary embodiment, the support mechanism 112 includes aplurality of data processing bots configured to automate rule creation,rule evaluation, data extraction, data analysis etc., for execution ofSCM related processing tasks. The support mechanism 112 may includehardware components or software components or a combination of hardwareand software components integrating multiple datasets through one ormore applications implemented on a cloud integration platform.

In an embodiment, the software component as a bot may be a computerprogram enabling an application to integrate with distinct data sourcedevices and systems by utilizing Artificial intelligence. The hardwareincludes the memory, the processor, control unit and other associatedchipsets especially dedicated for performing recalibration of datamodels to carry out rule creation, rule valuation, data extraction,classification, and one or more task execution in the EA when triggeredby the bots. The memory may include instruction that are executable bythe processor for causing the processor to execute the method ofexecuting one or more tasks in the EA.

The one or more processors 114 may be implemented as a chipset of chipsthat include separate and multiple analog and digital processors. Theprocessor may provide coordination of the other components, such ascontrolling user interfaces, applications run by devices, and wirelesscommunication by devices. The Processor may communicate with a userthrough control interface and display interface coupled to a display.The display may be, for example, a TFT LCD (Thin-Film-Transistor LiquidCrystal Display) or an OLED (Organic Light Emitting Diode) display, orother appropriate display technology. The display interface may compriseappropriate circuitry for driving the display to present graphical andother information to an entity/user. The control interface may receivecommands from a user and convert them for submission to the processor.In addition, an external interface may be provided in communication withprocessor, so as to enable near area communication of device with otherdevices. External interface may provide, for example, for wiredcommunication in some implementations, or for wireless communication inother implementations, and multiple interfaces may also be used.

In an embodiment, the underlying codeless platform architecture of thepresent invention enables one or more components of tool 109 to invokechanges in the configurable components of the codeless platform forcreating rules. The configurable components enable an applicationdeveloper user/a citizen developer user, a platform developer user and aSCM application user working with the SCM application to execute theoperations based on the rules created through the interface. The SCMapplication user or admin user triggers and interacts with thecustomization layer 105 for execution of one or more tasks throughapplication user machine 106, a function developer user or citizendeveloper user triggers and interacts with the application layer 104 tocreate rules for execution of the operation through citizen developermachine 106A, and a platform developer user through its computing device106B triggers the shared framework layer 103, the foundation layer 102and the data layer 101 to structure the platform for enabling codelessdevelopment of SCM applications thereby providing underlyingarchitecture for restricting of one or more applications.

In an embodiment, the codeless platform of the system 100 includescomponents such as tenant configurator, package version management,release manager, deployment manager and infrastructure configurator aspart of package management. These components provide window for citizendeveloper and platform developer to release the packages and providevisibility of the deployment pipeline. The tenant configurator enablesdeployment of packages to one or more clients across environments. Itprovides ability to create and manage details of customers through UI.It gives deployment experience through projects and environments tosupport the tenant concept. The Version management component managesmultiple versions of the packages and changes made by applicationdevelopers and platform developers. The component provides UI to look atmultiple versions of the package and compare versions. It manages minorand major versions. The release manager component is responsible formanaging, planning, scheduling, and controlling delivery throughout therelease lifecycle using other subcomponents and for Orchestrating entirepipeline with automation. The deployment manager component configuresand run delivery workflows for applications and platforms. It Createsstandardized deployment process to deploy predictable, high-qualityreleases. The component automates workflows, including versioning,application package generation, artifact management, and packagepromotion to different stages in the workflow. The infrastructureconfigurator component is responsible to provision services and databaserepositories as per application and loads. The component supportsautomation to provision infrastructure as per release and version.

In an exemplary embodiment, apart from application user interface,output of the system is exposed as API for third party digital platformsand applications. Since, the API is also consumed by bots and mobileapplications, it enables connection with blockchain networks throughmachine learning fragments.

In an example embodiment, the system 100 of the present inventionincludes a memory data store 111 having a plurality of databases asshown in FIG. 1 . The data store 111 includes a historical database 111Afor storing historical data including but not limited to historical SCMapplication data, historical rules data, historical data with identifierinformation and relationship information defining a relationship of oneor more data attributes of a task with one or more historical rules,historical documents data with plurality of data items etc. Thehistorical database 111A also includes classified historical datarelated to one or more document, file or function executed through theenterprise application. The one or more processors of the system areconfigured for performing one or more SCM processing task associated oneor more data scripts configured for generating the list of optimum rulesbased on a rule evaluation, wherein the one or more data scripts arestored in a data script database 111B and the data scripts are backendscripts created by a bot based on the first input and AI processing forenabling automation of identifying and generating the optimum rules fromthe plurality of rules.

In an embodiment, the system 100 of the present invention provides thehistorical database configured for storing historical data where theconnections of the data in the database are structured on AI basedmodel-driven flows incorporating reference to one or more identifiers tolink the data and rules within supply chain.

The data store 111 includes a machine learning model database 111Chaving one or more domain models including but not limited to conceptualor particular domain data models. The machine learning model includesclassification model, regression models, clustering model andrecommendation model. The data store 111 also includes a plurality ofSCM related data models that enable processing of data in enterpriseapplication. The data models include graph data models trained on graphstructures for semantic queries with nodes, edges and properties torepresent and store data. The data models also include a plurality oftraining models required to process the received input data foridentifying relationship with historical data stored in the historicaldatabase 111A. The data model also includes relational data model,document data model as relationship models for identification ofrelationships. The data store 111 also includes a rule creation syntaxdata library database 111D having a plurality of domain specific rulecreation syntax configured to create rule for processing one or moretasks in EA.

In an exemplary embodiment, the system for executing one or more tasksalso include blockchain based implementations. The system enables ruleengine to extend federated machine learning models to blockchainimplemented databases. The systems including third party systemsimplemented through blockchain network enables processing of thefederated machine learning model where, the federated machine learningmodel is configured to combine/federate output of one or more distinctmachine learning models. The rule creation interface may structure ruleswith identifier or security verification process before involving anychanges or executing the task. The identifier or verification may bethrough internet protocol (IP) address, security certificate data etc.,and the logical flow block with blockchain implementation includespublic key, primary key, to ensure secured communication through ablockchain network for executing the SCM application tasks. Further, theblockchain implemented system enables connection of one or more clientnodes with one or more server nodes through the blockchain network. Theblockchain network includes one or more data blocks connected to eachother and configured for storing SCM application data. The blockchainnetwork enables an integrator to integrate the AI engine with one ormore external entity systems.

The data store 111 also includes a graph database 111E configured tostore nodes and relationships. The graph database 111E is a specialized,single-purpose platform for creating and manipulating graphs. Graphscontain nodes, edges, and properties, all of which are used to representand store data.

The data store 111 also includes a plurality of registers 111F as partof the memory data store 111 for temporarily storing data from variousdatabases to enable transfer of data by the processors 114 between thedatabases as per the instructions of the AI engine 113 to enableprocessing of received input data for creation of rules to execute on ormore tasks.

The memory data store 111 may be a volatile, a non-volatile memory ormemory may also be another form of computer-readable medium, such as amagnetic or optical disk.

The memory store 111 may also include storage device capable ofproviding mass storage. In one implementation, the storage device may beor contain a computer-readable medium, such as a floppy disk device, ahard disk device, an optical disk device, or a tape device, a flashmemory or other similar solid-state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations.

Referring to FIG. 1A, a machine learning operation framework 100A ofmodel driven rule engine for dynamic data driven enterprise applicationis provided in accordance with an example embodiment of the invention.The machine learning model is standardized in codeless platform whichcan be accessed by rules engine. The machine learning models are trainedthrough workflows. Once the model is built, the machine learningoperations architecture 100A demonstrates how the model is genericallyexposed to the codeless platform through a ‘model-as-service’ interface.The machine learning (ML) operations workflow is designed in acloud-agnostic fashion and can be deployed anywhere. Since the ML modelsare trained and deployed as ‘model-as-service’ which makes themaccessible as REST microservices to rest of the codeless platform. TheREST microservices follow the same authentication/authorization (akaACS/TCS) making the ML model available under the same microservices. Themodel management is critical, and the ML model is ‘registered’, i.e. MLmodel is versioned and the higher-level application (viz. rules engine)can revert or different rules can refer to different versions of thesame ML model. Further the machine learning operations framework ensureto automatically trigger, retraining of the ML model, hence rulesreferring to ML benefit from latest insights from the data.

The framework 100A enables defining of input and output schema forapplying on a registered model of the framework. Further, the frameworkenables integration with microservices, integrate with multi-cloud,implementation of model-as-service as part of shared service layer, andwrapper custom model for specific machine learning models etc. Themachine learning operations enable model version control, end to endlineage tracking (with logs and Dashboard), for near real-time scoring,output response is provided as REST endpoint. For batch scoring, outputresponse is saved in database/datastore table and then used as RESTendpoint.

Referring to FIG. 1B is a rule engine block diagram 100B of the systemis provided in accordance with an embodiment of the invention. Theprimary layer of rule engine includes a plurality of components such asexecution engine, metadata parser, error handling, audit, stimulationengine, data manager, cache manager, approval, versions. The executionengine is configured to execute the rules after Rule Metadata is parsedby Parser component. The Metadata Parser is responsible to convert JSONdata received from Enterprise application and metadata, connecting todependent master data APIs, Converts into format as per ExecutionEngine. The Error Handling component manages Error Handling and capturelogs along with showing information to User. The Audit componentcaptures all transactions received for Rules. Data is used bystimulation engine that provides ability for Admin to mock rules outputafter any changes, reduces organization errors. It also connects with AIengine for Conflict resolution, Duplicates etc. The data manager isresponsible to manage Save and GET and connecting to underlying databaserepository. The Cache Manager is configured to manage Caching offrequent master data API, Rule configurations and settings. It alsoreduces unnecessary database calls and increases performance. Theapproval component is configured to enable approval of rules beforepublishing to applications. The Versions component allows versioning ofrules after every change.

In a related embodiment, the rule engine provides ability to GUI formanaging rules, approval for rules and listing of rules. The GUI hasvarious tool and criterion sets, groups, and also connects to API fortransactions. The GUI also provides navigation panel with list of ruletool sets. The API block for rule engine GUI manages rules, versions andapprovals. The API also invokes rule engine, accept enterprise data likePurchase order (PO), contract etc. The API enable rule evaluation andreal time response. The AI engine include various components related todata processing in the system. The AI engine enables creation of domainmodels, data models and training data sets based on the data models forprocessing data in enterprise applications.

Referring to FIG. 2 , a flowchart 200 depicting a method for executingone or more tasks in an enterprise application is provided in accordancewith an embodiment of the invention. The method includes the step of 201receiving at least one rule on a rule creation interface by a user toexecute one or more tasks wherein a rule creation syntax data library isprovided on the interface for enabling the user to create the at leastone rule. In step 202, identifying and triggering one or more machinelearning (ML) models related to the at least one rule for processing theat least one rule to execute the one or more tasks, wherein the one ormore machine learning models is integrated into a rules engine coupledto a processor for processing the at least one rule to execute the oneor more tasks.

In an embodiment, the one or more machine learning models includeclassification model, regression model, recommendation model andclustering/anomaly detection model. The classification model produces a“label” on the object being processed, e.g. a) produce sentiment on anews article: ‘positive’ or ‘negative’, b) Classify clause in acontract: ‘data privacy’, ‘assignment’, etc.; c) Classify a spendtransaction: ‘IT spend’, ‘office supply’, ‘lab material’, etc. This asupervised ML model, i.e. requires training data. The output of thismodel ‘label’ can be multi-valued, i.e. an object can be assigned tomultiple categories.

In an embodiment, the regression model predicts a ‘numerical value’,e.g. a) predict the risk in a range [0, 1] supplier's ability to meettimelines; b) predict the optimal price on an item; c) Forecast themonthly demand for next 6 months etc. This is a supervised ML model. The‘numerical value’ can be bounded like the risk score, probability in therange of [0, 1] or Unbounded like prices or Indexed values, e.g. 6different forecast values for next 6-months.

In an embodiment, the recommendation model recommends the nextstate/action/combination, e.g. a) Next state: Process routing ofinvoices based on item, payment amount, supplier, etc.; b) Action:Directly send invoice for executive approval; c) Combination: “you mayalso like to see . . . ” items in a retail website; d) Offers: Recommenda discount in retail shopping cart based on user profile and actions.This is a supervised ML model (training data is provided) orsemi-supervised (the model is being updated through userselections/actions). The output of this ML-model is interpretableaccording to the organization domain.

In an embodiment, the clustering/Anomaly detection model clusters a setof data points to understand the primary themes in the data and identifyoutliers/anomalies that don't fit into any clusters/themes, e.g. a)Cluster items that are typically ordered together in the same invoiceand identify fraud when a seemingly unrelated item appears; b)(Cybersecurity example): Cluster the department's login IP addresses andidentify any unusual login pattern from a different department. This isunsupervised ML model (i.e. no prior training data required) and itproduces two types of outputs (i) a set of clusters and the membershipof transactions/items for those clusters and (ii) a set ofitems/transaction that don't fit into any clusters.

In an embodiment, the one or more machine learning models create astandardized integration with each type of the one or more machinelearning models in the rules engine.

In an embodiment, the one or more machine learning models are trainedand deployed as model-as-service thereby enabling access to the modelsas microservice of a codeless platform.

In an embodiment, the invention includes creating a training data modelby retrieving the historical data elements from database, cleansing thehistorical data elements for obtaining normalized historical data,extracting a plurality of categories from the normalized historical datafor creating taxonomy of relationships associated with the one or moredata attributes, fetching a plurality of code vectors from thenormalized historical data wherein the code vectors correspond to eachof the extracted categories of the relationships associated with the oneor more data attributes, extracting a plurality of distinct words fromthe normalized historical data to create a list of variables,transforming normalized historical data into a training data matrixusing the list of variables, and creating the training relationship datamodel from the classification code vectors and the training data matrixby using the machine learning engine (MLE) and the AI engine.

In a related embodiment, the relationship between the task to beexecuted and the rule configured to execute the task is identified basedon relationship between one or more data attribute of the task and theone or more historical data elements. The determination of therelationship is dependent on one or more trained data model forprocessing the received task.

In an example embodiment, the invention includes applying relationaldata model (RDM) algorithms to train one or more relational data modelfor the normalized historical data by using machine learning engine(MLE), applying document model (DM) algorithms to obtain document datamodels by using machine learning engine (MLE), applying graphical datamodel (GDM) algorithms to obtain graphical data models by using machinelearning engine (MLE), and saving RDM, DM and GDM models as the trainingrelationship models for identification of relationships in a trainingmodel database.

In an exemplary embodiment, the system of the invention is responsibleto generate operational process workflow like business process throughAI using model driven pattern. The models are generated using historicalworkflow. Modeling decision of existing business process are driventhrough machine learning (ML) models. The ML makes predictions based onthe historical workflow data as predictions brings knowledge ofoperational/business process and interpretation and the predictions arenon-deterministic.

Further, data driven model relies on larger volume of data. The datadriven AI pattern is driven by historical knowledge data whiledeterministic are driven through expert and deep learner's enginefocused on specific problem areas. The data driven AI patterns relies onsmaller data sets which are accurate as they are focused on procurementand supply chain workflows and targeted for various industry verticals.

In a related embodiment, the model-driven AI flow enables users toaccess data in a unified manner regardless of the underlying data store.Data store queries (e.g., relational, or file systems) are significantlystreamlined for structuring the workflow. The essential aspects of timeand space, data normalizing, versioning, and tracking are all handled bythe system.

In an embodiment, the rule creation interface is a graphical userinterface (GUI) having one or more graphical elements configured toreceive one or more inputs for structuring the rule wherein the GUIincludes a UI Expression builder configured for processing complexfunctions, Boolean operations, and logical operators.

In an embodiment, the method of executing one or more tasks in thedynamic data driven enterprise application includes generating the GUI,wherein the GUI includes an input component for receiving an inputindicating the at least one ride to execute the one or more tasksthrough the GUI. The method further includes receiving the inputindicating the at least one rule via the input component, wherein the atleast one rule represents a set of syntax structured to execute the oneor more tasks, identifying and triggering the one or more machinelearning models based on the input for obtaining an output to berendered within the GUI indicating execution of the one or more tasks.

In an embodiment, the set of syntax is previously generated by analyzinghistorical data related to the one or more tasks through the AI engine.

In an embodiment the invention includes checking by an AI engine, if theat least one rule is existing in a historical rules database andtriggering a. notification on the GUI informing existence of a duplicaterule wherein one or more data attributes associated with the one or moretasks to be executed is analyzed by the AI engine for determiningexistence of duplicate rules.

The rule creation syntax data library includes one or more componentsrelated to a plurality enterprise application function, wherein thecomponents include condition, group, array, properties, criteria,logical operators, functional or operational components.

In an embodiment, the rules engine is configured for using the one ormore machine learning models, wherein the rules engine is configured fordirecting to one or more machine learning models as service end point,creating a data mapping of machine learning model to a schema of datathat a machine learning service requires, and providing mapping ofoutput of the machine learning service.

In an embodiment, the one or more tasks include demand planning, supplyplanning, inventory management, warehouse management, contract lifecyclemanagement, sourcing, forecasting, cost modelling, transportationmanagement, product life cycle management, purchase Order and salesmanagement, invoicing, work order management, receivables, suppliercollaboration management, in the enterprise application including an ERPor a supply chain management application.

Referring to FIG. 2A, a flowchart depicting a method for executing oneor more tasks through optimum rule in an enterprise application isprovided in accordance with an embodiment of the invention. The methodincludes the step 201A of generating a graphical user interface (GUI),wherein the GUI includes a first input component for receiving a firstinput indicating the one or more tasks to be executed through the GUI.In step 202A, receiving the first input indicating the one or more tasksto be executed via the first input component, wherein a processingdevice identifies a plurality of rules configured for executing the oneor more tasks, the processing device being coupled to an AI engine. Instep 203A, in response to identification of a plurality of rulesconfigured to execute the one or more tasks, generating and renderingwithin the GUI a list of optimum rules from the plurality of rules as afirst output. In step 204A, receiving a second input indicating at leastone rule from the list of optimum rules for executing the one or moretasks via a second input component, wherein the at least one rulerepresents a set of syntax structured to execute the one or more tasks,wherein the set of syntax is previously generated by analyzinghistorical data related to the one or more tasks through the AI engine.In step 205A, triggering one or more machine learning models based onthe second input to obtain a second output from the one or more machinelearning models to be rendered within the GUI indicating execution ofthe one or more tasks, wherein the one or more machine learning modelsis integrated into a rule engine coupled to the processing device forprocessing the at least one rule to execute the one or more tasks.

In an embodiment, the AI engine is configured to map the first inputwith a historical dataset of rules to identify the optimum rules to berendered on the GUI for selection by the user.

In a related embodiment, the AI processing includes integration of deeplearning, predictive analysis, information extraction, planning,scheduling, impact analysis and robotics for analysis of the first inputdata to identify the optimum rules from the plurality of rules.

In an embodiment, the input component includes a search engineconfigured to receive the first input, wherein the AI engine coupled tothe processing device identifies intent from the received first input togenerate the first output.

In an embodiment, the processing device is configured to generate a rulecreation interface within the graphical user interface (GUI) in responseto the received first input wherein the rule creation interface isgenerated if the processing device determines absence of the rules toprocess the one or more tasks thereby enabling the user to create therules on the rule creation interface.

In an exemplary embodiment, the invention provides the graphical userinterface (GUI) for executing one or more tasks in the dynamic datadriven enterprise application. The GUI includes one or more graphicalelements depicting one or more data objects, and one or more inputcomponents of the graphical elements configured for receiving one ormore inputs through the interface. The one or more data objects includerule creation data object, rule evaluation data object and rule testingdata object. The one or more data objects generate a projection of theone or more task within the GUI through the graphical elements. The oneor more input components receive one or more input associated with theone or more data object for execution of the one or more tasks.

Referring to FIG. 3 , a user interface 300 for rule creation is providedin accordance with an example embodiment of the invention. The userinterface 300 includes rule creation by adding conditions or groups asshown. The user interface 300 enables rule creation by machine learningmodel driven rule engine. The user interface 300 executes rules forverification of supplier rating as a task to be executed in theenterprise application. The user interface 300 enables addition ofComponents in AND or OR conditions. It also handles complex conditionsusing a GROUP function including rule with all And Condition, Rule withAnd and OR condition, and rule with nested groups.

Referring to FIG. 3A, a user interface 300A showing conversion of visualrepresentation to code for debugging is provided in accordance with anexample embodiment of the invention. The user interface shows codes suchas documentobject not undefined, documentobject is hosted, documentobjpunched etc.

Referring to FIG. 3B, a user interface (UI) expression builder 300B ofthe system is provided in accordance with an example embodiment of theinvention. The UI Expression builder helps in writing complex functions,Boolean operations, and mathematical operators which can otherwise bedone only through complex if-then-else programming by a developer.

Referring to FIG. 4 and FIG. 4A, user interface (400, 400A) showing alist of rules including system configured rules, rules created on a rulecreation user interface and rules created by extension tools or UIexpression builder is provided in accordance with an example embodimentof the invention. The system configured rules include GDPR, ISO,security, domain, organization or system compliance rules generatedbased on historical dataset of the enterprise application, validationrules and AI data models.

Referring to FIG. 5 , a rule creation interface 500 for purchase order(PO) creation task is provided in accordance with an embodiment of theinvention. The interface includes addition of one or more conditions tostructure the rule through one or more properties associated with rulecreation.

Referring to FIG. 5A, a rule creation interface or graphical interface(GUI) 500A associating a PO value and showing properties and rulescharacteristics is provided in accordance with an embodiment of theinvention. The rule includes processing of properties including source,operator, type, value etc., associated with field rules to execute thetask.

Referring to FIG. 5B, a rule creation interface or graphical interface(GUI) 500B showing rule for checking supplier rating with logicaloperators is provided in accordance with an embodiment of the invention.The rule includes processing of properties including one or morecriteria like buyer rating, percentage of returns etc., to executesupplier rating check task.

Referring to FIG. 5C, a rule creation interface or graphical userinterface (GUI) 500C creating rule for duplicate finder task is providedin accordance with an embodiment of the invention. The rule includesprocessing of properties including one or more criteria like same emailId, same phone number etc., to execute duplicate finder task.

Referring to FIG. 5D, a rule creation interface or graphical userinterface (GUI) 500D creating rule for checking probability of on timedelivery considering the changing parameters is provided in accordancewith an embodiment of the invention. The rule includes processing ofproperties with true/false conditions and support of AI engineprocessing the historical datasets associated with past performance of asupplier while verifying supplier risk rating as a task in theenterprise application.

Referring to FIG. 5E, a rule creation interface 500E creating a set oftest cases to run values and find the outcome from the rule to ensurethat rule is working and delivering results is provided in accordancewith an embodiment of the invention. The user interface enables testingand debugging of all components of rules by creating test cases andproviding input values to test outcome on the GUI.

Referring to FIG. 6 , a flow diagram 600 depicting creation of purchaseorder related task is provided in accordance with an example embodimentof the invention. The PO (purchase order) as a task is submitted in theenterprise application. The rule engine evaluates the basic mandatoryfield, approval validations, and it is determined that the expecteddelivery time is January 10 with a grace period of 1 week as configured.The actual delivery time is evaluated by AI engine based on historicaldelivery data for the same item by the supplier. The probability outputof the AI is used to validate against the date entered by Purchase order(PO) author/user. The AI engine also considers externalconstrains/factors including but not limited to time of the year,logistic issues, weather etc. The PO submit to supplier is put on holdif the expected delivery time does not match based on the historicalperformance. This enables dynamic rule evaluations which was notpossible with static rule conditions earlier.

Referring to FIG. 7 , a flow diagram 700 depicting identification ofconflicting rules while executing a task is provided in accordance withan embodiment of the invention. The graphical user interface (GUI) isconfigured to receive input data and render output data within the GUIto create rules, identify conflicting rules, ascertain default rules,and identify duplicate rules.

In an example scenario, rules keep grow in the EA and admin userunintentionally start creating rules having similar validations or hasoverlapping criterion. For e.g. a) Contract should be approved by User 1if amount is between USD 50,000 to USD 75,000; b) Contract should beapproved by User 2 if amount is between USD 80,000 to USD 100,000, etc.In case, during contract execution, the contract amount is USD 76,000then the execution of the contract is ignored, and it will fall throughwithout proper validations as the system will not be able to identifyauthorized approver. In such situations, the rule includes one or moredynamic values associated with one or more rule conditions as mentionedabove. The AI engine invokes the one or more dynamic rules forprocessing the rule to execute the task. The AI engine is invoked whilean Admin User is creating rules. It will identify duplicate of gaps inrule criterion dynamically and not with any static condition.

In an advantageous aspect, the system and method of the presentinvention enables extending rules engine to let users write rules thatcan involve machine learning based decisions as rules refer to MLderived decisions. The invention injects data driven aspect into rulesthrough Machine learning models. Further, the invention standardizesintegration of any ML model into a rule engine and other framework(s).Furthermore, it Wraps ML models into specific patterns for rule-engineintegration.

In another advantageous aspect, the rules Engine provides uniquebenefits to end-users focusing on codeless platform. The system andmethod of the invention provides a graphical user interface with simpleinteraction to non-technical users to build Rules. The inventionprovides a visual and easy to interpret representation of rules. Itautomatically generates the code version in the background, whileassisting user to validate and test it through simple actions on screen.The system further provides support for AI/ML engines as priorityfeature within rule creation interface to handle complex use cases. Thesystem defines multiple complex conditions and actions throughArtificial Intelligence/self-learning algorithms and build complicatedrules using machine learning to automate workflows.

In an exemplary embodiment, the application user interface (UI) and therule creation user interface of the entity machines enables cognitivecomputing to improve interaction between user and an enterprise orsupply chain application(s). The interface improves the ability of auser to use the computer machine itself. Since, the interface triggersconfigurable components of the platform architecture along withcomponents of the rule creation user interface for executing one or moretasks including but not limited to creation of Purchase order, Contractlifecycle management operations, Warehouse management operations,inventory management operations etc., at the same instant, the interfacethereby enables a user to take informed decision or undertake anappropriate strategy for adjusting workflow for execution of operations.By structuring operations and application functions through a layeredplatform architecture and eliminating multiple cross function layers,repetitive processing tasks and recordation of information to get adesired data or operational functionality, which would be slow andcomplex, the user interface is more user friendly and improves thefunctioning of the existing computer systems.

The present invention uses Artificial intelligence, processorchestration and layered platform architecture where the entireoperational logic in the service is transformed into engine reducingcomplex logic at the back end. The sequence flow is translated in theengine through GUI. It is very helpful to manage multitenantapplications. The system includes both backend and frontend components(UI components, rules engine and workflow) being restructured. Thesystem offers productivity gain and accelerates implementation cycle.The system empowers functional admin to configure UI, add fields,layouts, rule engine and workflows without development efforts. Thesingle page application framework provides better user experience,ability to configure localization and theming from admin portal withoutengineering support.

In an exemplary embodiment, the present invention may be a platformarchitecture, application framework, system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention. The media has embodied therein, for instance,computer readable program code (instructions) to provide and facilitatethe capabilities of the present disclosure. The article of manufacture(computer program product) can be included as a part of a computersystem/computing device or as a separate product.

The computer readable storage medium can retain and store instructionsfor use by an instruction execution device i.e. it can be a tangibledevice. The computer readable storage medium may be, for example, but isnot limited to, an electromagnetic storage device, an electronic storagedevice, an optical storage device, a semiconductor storage device, amagnetic storage device, or any suitable combination of the foregoing. Anon-exhaustive list of more specific examples of the computer readablestorage medium includes the following: a hard disk, a random accessmemory (RAM), a portable computer diskette, a read-only memory (ROM), aportable compact disc read-only memory (CD-ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a digitalversatile disk (DVD), a static random access memory (SRAM), a floppydisk, a memory stick, a mechanically encoded device such as punch-cardsor raised structures in a groove having instructions recorded thereon,and any suitable combination of the foregoing. A computer readablestorage medium, as used herein, is not to be construed as beingtransitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the internet, a local area network(LAN), a wide area network (WAN) and/or a wireless network. The networkmay comprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

1. A method for executing one or more tasks in an enterpriseapplication, the method comprising: generating a graphical userinterface (GUI), wherein the GUI includes a first input component forreceiving a first input indicating the one or more tasks to be executedthrough the GUI; receiving the first input indicating the one or moretasks to be executed via the first input component, wherein a processingdevice identifies a plurality of rules configured for executing the oneor more tasks, the processing device being coupled to an ArtificialIntelligence (AI) engine; in response to identifi cad on of a pluralityof rules configured to execute the one or more tasks, generating andrendering within the GUI a list of optimum rules from the plurality ofrules as a first output, receiving a second input indicating at leastone rule from the list of optimum rules for executing the one or moretasks via a second input component. wherein the at least one rulerepresents a set of syntax structured to execute the one or more tasks,wherein the set of syntax is previously generated by analyzinghistorical data related to the one or more tasks through the AI engine;and triggering one or more machine learning models based on the secondinput to obtain a second output from the one or more machine learningmodels to be rendered within the GUI indicating execution of the one ormore tasks, wherein the one or more machine learning models isintegrated into a rule engine coupled to the processing device forprocessing the at least one rule to execute the one or more tasks. 2.The method of claim 1, wherein the one or more machine learning modelsinclude classification model, regression model, recommendation model andclustering/anomaly detection model.
 3. The method of claim 2, whereinthe one or more machine learning models create a standardizedintegration with each type of the one or more machine learning models inthe rule engine.
 4. The method of claim 3, wherein the one or moremachine learning models are trained and deployed as model-as-servicethereby enabling access to the models as microservice of a codelessplatform.
 5. The method of claim 4, further comprising: one or more datascripts configured for generating the list of optimum rules based on arule evaluation, wherein the one or more data scripts are backendscripts created by a bot based on the first input and AI processing forenabling automation of identifying and generating the optimum rules fromthe plurality of rules.
 6. The method of claim 5, wherein the AI engineis configured to map the first input with a historical dataset of rulesto identify the optimum rules to be rendered on the GUI for selection bythe user wherein the AI processing includes integration of deeplearning, predictive analysis, information extraction, planning,scheduling, impact analysis and robotics for analysis of the first inputdata to identify the optimum rules from the plurality of rules.
 7. Themethod of claim 6, wherein the input component includes a search engineconfigured to receive the first input, wherein the AI engine coupled tothe processing device identifies intent from the received first input togenerate the first output.
 8. The method of claim 7, wherein theprocessing device is configured to generate a rule creation interfacewithin the graphical user interface (GUI) in response to the receivedfirst input wherein the rule creation interface is generated if theprocessing device determines absence of the rules to process the one ormore tasks thereby enabling the user to create the rules on the rulecreation interface.
 9. The method of claim 8, further comprises:receiving at least one rule on the rule creation interface by a user toexecute the one or more tasks wherein a rule creation syntax datalibrary is provided on the interface for enabling the user to create theat least one rule; and identifying and triggering one or more machinelearning (ML) models related to the at least one rule for processing theat least one rule to execute the one or more tasks.
 10. The method ofclaim 9, wherein the at least one rule includes system configured rulescreated by an enterprise application codeless platform, rules created byextension tools, and rules created on the rules creation interface. 11.The method of claim 10, wherein the system configured rules includeGDPR, ISO, security, domain, organization or system compliance rulesgenerated based on historical dataset of the enterprise application,validation rules and Al data models.
 12. The method of claim 11, whereinthe GUI is configured to receive input data and render output datawithin the GUI to create rules, identify conflicting rules, ascertaindefault rules, identify duplicate rules, test and debug all componentsof rules by creating test cases and providing input values to testoutcome on the GUI.
 13. The method of claim 12, wherein the rulecreation syntax data library includes one or more components related toa plurality of enterprise application functions, wherein the componentsinclude condition, group, array, properties, criteria, logicaloperators, functional or operational components.
 14. The method of claim13, wherein the one or more tasks include demand planning, supplyplanning, inventory management, warehouse management, contract lifecyclemanagement, sourcing, forecasting, cost modelling, transportationmanagement, product life cycle management, Purchase Order and salesmanagement, invoicing, work order management, receivables, suppliercollaboration management, in the enterprise application including an ERPor a supply chain management application.
 15. The method of claim 14,wherein the at least one rule includes one or more dynamic valuesassociated with one or more rule conditions wherein the one or moredynamic values is invoked by the AI engine for processing the rule toexecute the one or more task.
 16. A system for executing one or moretasks in an enterprise application, the system comprising: a processingdevice; and a memory device including instructions that are executableby the processing device for causing the processing device to: generatea GUI, wherein the GUI includes a first input component for receiving afirst input indicating the one or more tasks to be executed through theGUI; receive the first input indicating the one or more tasks to beexecuted via the first input component, wherein an AI engine coupled tothe processing device identifies a plurality of rules configured forexecuting the one or more tasks; in response to identification of aplurality of rules configured to execute the one or more tasks, generateand render within the GUI, a list of optimum rules from the plurality ofrules; receive a second input indicating at least one rule from theoptimum rules for executing the one or more tasks via a second inputcomponent, wherein the at least one rule represents a set of syntaxstructured to execute the one or more tasks, wherein the set of syntaxis previously generated by analyzing historical data related to the oneor more tasks through the AI engine; and trigger one or more machinelearning models based on the second input to obtain a second output fromthe one or more machine learning models to be rendered within the GUIindicating execution of the one or more task, wherein the one or moremachine learning models are integrated into a rule engine coupled to theprocessing device for processing the at least one rule to execute theone or more tasks.
 17. The system of claim 16, wherein the processingdevice is configured to: receive at least one rule on the rule creationinterface by a user to execute the one or more tasks wherein a rulecreation syntax data library is provided on the interface for enablingthe user to create the at least one rule; and identifying and triggeringone or more machine learning (ML) models related to the at least onerule for processing the at least one rule to execute the one or moretasks.
 18. The system of claim 17, wherein the rule creation interfaceis a graphical user interface (GUI) that includes: one or more graphicalelements depicting one or more data objects including a rule creationdata object, a rule evaluation data object and a rule testing dataobject wherein the one or more data objects generate at least one rulewithin the GUI through the graphical elements; and one or more inputcomponents of the graphical elements configured for receiving one ormore inputs associated with the one or more data object for execution ofthe one or more tasks.
 19. The system of claim 18, further comprises ablockchain network enabling processing of a federated machine learningmodel wherein the federated machine learning model is configured tocombine/federate output of one or more distinct machine learning models.20. A non-transitory computer program product for executing one or moretasks in an enterprise application, the computer program productcomprising a non-transitory computer readable storage medium havinginstructions embodied therewith, the instructions when executed by oneor more processors causes the one or more processors to: generate a GUI,wherein the GUI includes a first input component for receiving a firstinput indicating the one or more tasks to be executed through the GUI;receive the first input indicating the one or more tasks to be executedvia the first input component, wherein an AI engine coupled to theprocessing device identifies a plurality of rules configured forexecuting the one or more tasks; in response to identification of aplurality of rules configured to execute the one or more tasks, generateand render within the GUI, a list of optimum rules from the plurality ofrules; receive a second input indicating at least one rule from theoptimum rules for executing the one or more tasks via a second inputcomponent, wherein the at least one rule represents a set of syntaxstructured to execute the one or more tasks, wherein the set of syntaxis previously generated by analyzing historical data related to the oneor more tasks through the AI engine; and trigger one or more machinelearning models based on the second input to obtain a second output fromthe one or more machine learning models to be rendered within the GUIindicating execution of the one or more task, wherein the one or moremachine learning models are integrated into a rule engine coupled to theprocessing device for processing the at least one rule to execute theone or more tasks.